{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b236e0a",
   "metadata": {},
   "source": [
    "# Sweeps - Impedance, scattering and admittance (Z S Y) matrices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced70c0a",
   "metadata": {},
   "source": [
    "### Prerequisite\n",
    "You need to have a working local installation of Ansys"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4c0b1a",
   "metadata": {},
   "source": [
    "## 1. Perform the necessary imports and create a QDesign in Metal first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac34fec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a672f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, draw\n",
    "from qiskit_metal import MetalGUI, Dict, Headings\n",
    "import pyEPR as epr\n",
    "from qiskit_metal.analyses.simulation import ScatteringImpedanceSim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e7b9b1f",
   "metadata": {},
   "source": [
    "## Create the design in Metal\n",
    " Set up a design of a given dimension.  Create a design by specifying the chip size and open Metal GUI.  Dimensions will be respected in the design rendering.  Note the chip design is centered at origin (0,0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "645c9820",
   "metadata": {},
   "outputs": [],
   "source": [
    "design = designs.DesignPlanar({}, True)\n",
    "design.chips.main.size['size_x'] = '2mm'\n",
    "design.chips.main.size['size_y'] = '2mm'\n",
    "\n",
    "gui = MetalGUI(design)\n",
    "\n",
    "# Perform the necessary imports.\n",
    "from qiskit_metal.qlibrary.couplers.coupled_line_tee import CoupledLineTee\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander\n",
    "from qiskit_metal.qlibrary.qubits.transmon_pocket import TransmonPocket\n",
    "from qiskit_metal.qlibrary.tlines.straight_path import RouteStraight\n",
    "from qiskit_metal.qlibrary.terminations.open_to_ground import OpenToGround"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9558f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To create plots after geting solution data.\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2387e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add 2 transmons to the design.\n",
    "options = dict(\n",
    "    # Some options we want to modify from the deafults\n",
    "    # (see below for defaults)\n",
    "    pad_width='425 um',\n",
    "    pocket_height='650um',\n",
    "    # Adding 4 connectors (see below for defaults)\n",
    "    connection_pads=dict(a=dict(loc_W=+1, loc_H=+1),\n",
    "                         b=dict(loc_W=-1, loc_H=+1, pad_height='30um'),\n",
    "                         c=dict(loc_W=+1, loc_H=-1, pad_width='200um'),\n",
    "                         d=dict(loc_W=-1, loc_H=-1, pad_height='50um')))\n",
    "\n",
    "## Create 2 transmons\n",
    "q1 = TransmonPocket(design,\n",
    "                    'Q1',\n",
    "                    options=dict(pos_x='+1.4mm',\n",
    "                                 pos_y='0mm',\n",
    "                                 orientation='90',\n",
    "                                 **options))\n",
    "q2 = TransmonPocket(design,\n",
    "                    'Q2',\n",
    "                    options=dict(pos_x='-0.6mm',\n",
    "                                 pos_y='0mm',\n",
    "                                 orientation='90',\n",
    "                                 **options))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f4efad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add 2 hangers consisting of capacitively coupled transmission lines.\n",
    "TQ1 = CoupledLineTee(design,\n",
    "                     'TQ1',\n",
    "                     options=dict(pos_x='1mm',\n",
    "                                  pos_y='3mm',\n",
    "                                  coupling_length='500um',\n",
    "                                  coupling_space='1um'))\n",
    "TQ2 = CoupledLineTee(design,\n",
    "                     'TQ2',\n",
    "                     options=dict(pos_x='-1mm',\n",
    "                                  pos_y='3mm',\n",
    "                                  coupling_length='500um',\n",
    "                                  coupling_space='1um'))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9291dc36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add 2 meandered CPWs connecting the transmons to the hangers.\n",
    "ops = dict(fillet='90um')\n",
    "design.overwrite_enabled = True\n",
    "\n",
    "options1 = Dict(total_length='8mm',\n",
    "                hfss_wire_bonds=True,\n",
    "                pin_inputs=Dict(start_pin=Dict(component='TQ1',\n",
    "                                               pin='second_end'),\n",
    "                                end_pin=Dict(component='Q1', pin='a')),\n",
    "                lead=Dict(start_straight='0.1mm'),\n",
    "                **ops)\n",
    "\n",
    "options2 = Dict(total_length='9mm',\n",
    "                hfss_wire_bonds=True,\n",
    "                pin_inputs=Dict(start_pin=Dict(component='TQ2',\n",
    "                                               pin='second_end'),\n",
    "                                end_pin=Dict(component='Q2', pin='a')),\n",
    "                lead=Dict(start_straight='0.1mm'),\n",
    "                **ops)\n",
    "\n",
    "meanderQ1 = RouteMeander(design, 'meanderQ1', options=options1)\n",
    "meanderQ2 = RouteMeander(design, 'meanderQ2', options=options2)\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2ad7438f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add 2 open to grounds at the ends of the horizontal CPW.\n",
    "otg1 = OpenToGround(design, 'otg1', options=dict(pos_x='3mm', pos_y='3mm'))\n",
    "otg2 = OpenToGround(design,\n",
    "                    'otg2',\n",
    "                    options=dict(pos_x='-3mm', pos_y='3mm', orientation='180'))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()\n",
    "\n",
    "# Add 3 straight CPWs that comprise the long horizontal CPW.\n",
    "\n",
    "ops_oR = Dict(hfss_wire_bonds=True,\n",
    "              pin_inputs=Dict(start_pin=Dict(component='TQ1', pin='prime_end'),\n",
    "                              end_pin=Dict(component='otg1', pin='open')))\n",
    "ops_mid = Dict(hfss_wire_bonds=True,\n",
    "               pin_inputs=Dict(start_pin=Dict(component='TQ1',\n",
    "                                              pin='prime_start'),\n",
    "                               end_pin=Dict(component='TQ2', pin='prime_end')))\n",
    "ops_oL = Dict(hfss_wire_bonds=True,\n",
    "              pin_inputs=Dict(start_pin=Dict(component='TQ2',\n",
    "                                             pin='prime_start'),\n",
    "                              end_pin=Dict(component='otg2', pin='open')))\n",
    "\n",
    "cpw_openRight = RouteStraight(design, 'cpw_openRight', options=ops_oR)\n",
    "cpw_middle = RouteStraight(design, 'cpw_middle', options=ops_mid)\n",
    "cpw_openLeft = RouteStraight(design, 'cpw_openLeft', options=ops_oL)\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "intended-celebration",
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bfca232",
   "metadata": {},
   "source": [
    "## 2. Render the qubit from Metal into the HangingResonators design in Ansys. <br>ScatteringImpedanceSim will  open the simulation software. Then will connect, activate the design, add a setup.  \n",
    "\n",
    "Review and update the setup. For driven modal you will need to define not only the simulation convergence parameters, but also the frequency sweep.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f302b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "em1 = ScatteringImpedanceSim(design, \"hfss\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f02f684-c628-4efb-a82e-129c1323d88e",
   "metadata": {},
   "source": [
    "Customizable parameters and default values for HFSS (driven  modal):\n",
    "\n",
    "    freq_ghz=5 (simulation frequency)\n",
    "    name=\"Setup\" (setup name)\n",
    "    max_delta_s=0.1 (absolute value of maximum difference in scattering parameter S)\n",
    "    max_passes=10 (maximum number of passes)\n",
    "    min_passes=1 (minimum number of passes)\n",
    "    min_converged=1 (minimum number of converged passes)\n",
    "    pct_refinement=30 (percent refinement)\n",
    "    basis_order=1 (basis order)\n",
    "    vars (global variables to set in the renderer)\n",
    "    sweep_setup (all the parameters of the sweep)\n",
    "    name=\"Sweep\" (name of sweep)\n",
    "    start_ghz=2.0 (starting frequency)\n",
    "    stop_ghz=8.0 (stopping frequency)\n",
    "    count=101 (total number of frequencies)\n",
    "    step_ghz=None (frequency step size)\n",
    "    type=\"Fast\" (type of sweep)\n",
    "    save_fields=False (whether or not to save fields)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "834584b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Setup',\n",
       " 'reuse_selected_design': True,\n",
       " 'reuse_setup': True,\n",
       " 'freq_ghz': 5,\n",
       " 'max_delta_s': 0.1,\n",
       " 'max_passes': 10,\n",
       " 'min_passes': 1,\n",
       " 'min_converged': 1,\n",
       " 'pct_refinement': 30,\n",
       " 'basis_order': 1,\n",
       " 'vars': {'Lj': '10 nH', 'Cj': '0 fF'},\n",
       " 'sweep_setup': {'name': 'Sweep',\n",
       "  'start_ghz': 2.0,\n",
       "  'stop_ghz': 8.0,\n",
       "  'count': 101,\n",
       "  'step_ghz': None,\n",
       "  'type': 'Fast',\n",
       "  'save_fields': False}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To view the values for defaults. \n",
    "em1.setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "de6fa174",
   "metadata": {},
   "outputs": [],
   "source": [
    "em1.setup.name = \"Sweep_DrivenModal_setup\"\n",
    "em1.setup.freq_ghz = 6.0\n",
    "em1.setup.max_delta_s = 0.05\n",
    "em1.setup.max_passes = 12\n",
    "em1.setup.min_passes = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d54a538",
   "metadata": {},
   "source": [
    "\n",
    "Add a frequency sweep to a driven modal setup.<br>\n",
    "From QHFSSRenderer.add_sweep doc_strings.  Please go to doc_strings to get the latest information.  \n",
    "\n",
    "Args:<br>\n",
    "    setup_name (str, optional): Name of driven modal simulation Sweep.\n",
    "                            Defaults to \"Setup\".<br>\n",
    "    start_ghz (float, optional): Starting frequency of sweep in GHz.\n",
    "                            Defaults to 2.0.<br>\n",
    "    stop_ghz (float, optional): Ending frequency of sweep in GHz.\n",
    "                            Defaults to 8.0.<br>\n",
    "    count (int, optional): Total number of frequencies.\n",
    "                            Defaults to 101.<br>\n",
    "    step_ghz (float, optional): Difference between adjacent\n",
    "                            frequencies. Defaults to None.<br>\n",
    "    name (str, optional): Name of sweep. Defaults to \"Sweep\".<br>\n",
    "    type (str, optional): Type of sweep. Defaults to \"Fast\".<br>\n",
    "    save_fields (bool, optional): Whether or not to save fields.\n",
    "                        Defaults to False.<br>\n",
    "                        \n",
    "                        \n",
    "From pyEPR.HfssSetup.insert_sweep(), please go to this method to get the latest documentation.   \n",
    "You should provide either step_ghz or count when inserting an HFSS driven model freq sweep. Do not provide both or neither!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9f5f6acd-4809-4455-b2cf-de0fb0c83059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Sweep',\n",
       " 'start_ghz': 2.0,\n",
       " 'stop_ghz': 8.0,\n",
       " 'count': 101,\n",
       " 'step_ghz': None,\n",
       " 'type': 'Fast',\n",
       " 'save_fields': False}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To view the values for defaults. \n",
    "em1.setup.sweep_setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9135df20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Sweep_DrivenModal_setup',\n",
       " 'reuse_selected_design': True,\n",
       " 'reuse_setup': True,\n",
       " 'freq_ghz': 6.0,\n",
       " 'max_delta_s': 0.05,\n",
       " 'max_passes': 12,\n",
       " 'min_passes': 2,\n",
       " 'min_converged': 1,\n",
       " 'pct_refinement': 30,\n",
       " 'basis_order': 1,\n",
       " 'vars': {'Lj': '10 nH', 'Cj': '0 fF'},\n",
       " 'sweep_setup': {'name': 'Sweep_options__dm_sweep',\n",
       "  'start_ghz': 4.0,\n",
       "  'stop_ghz': 9.0,\n",
       "  'count': 5001,\n",
       "  'step_ghz': None,\n",
       "  'type': 'Interpolating',\n",
       "  'save_fields': False}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "em1.setup.sweep_setup.name=\"Sweep_options__dm_sweep\"\n",
    "em1.setup.sweep_setup.start_ghz=4.0\n",
    "em1.setup.sweep_setup.stop_ghz=9.0\n",
    "em1.setup.sweep_setup.count=5001\n",
    "em1.setup.sweep_setup.type=\"Interpolating\"\n",
    "\n",
    "\n",
    "em1.setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d00fa8c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the buffer width at the edge of the design to be 0.5 mm \n",
    "# in both directions.\n",
    "em1.setup.renderer.options['x_buffer_width_mm'] = 0.5\n",
    "em1.setup.renderer.options['y_buffer_width_mm'] = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ce9dbaa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#     qcomp_name (str): A component that contains the option to be swept.\n",
    "#     option_name (str): The option within qcomp_name to sweep.\n",
    "#     option_sweep (list): Each entry in the list is a value for\n",
    "#                 option_name.\n",
    "#     qcomp_render (list): The component to render to simulation.\n",
    "#     open_terminations (list): Identify which kind of pins. Follow the\n",
    "#                 details from renderer QQ3DRenderer.render_design, or\n",
    "#                 QHFSSRenderer.render_design.\n",
    "#     port_list (list): List of tuples of jj's that shouldn't\n",
    "#                     be rendered.  Follow details from\n",
    "#                     renderer in QHFSSRenderer.render_design.\n",
    "#     jj_to_port (list): List of junctions (qcomp, qgeometry_name,\n",
    "#                         impedance, draw_ind) to render as lumped ports\n",
    "#                         or as lumped port in parallel with a sheet\n",
    "#                         inductance.    Follow details from renderer\n",
    "#                         in QHFSSRenderer.render_design.\n",
    "#     ignored_jjs (Union[list,None]): This is not used by all renderers,\n",
    "#                  just hfss.\n",
    "#     design_name(str): Name of design (workspace) to use in project.\n",
    "#     box_plus_buffer(bool): Render the entire chip or create a\n",
    "#                 box_plus_buffer around the components which are rendered.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "80500d37",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "design_name= \"Sweep_DrivenModal\"\n",
    "qcomp_render = [] # Means to render everything in qgeometry table.\n",
    "open_terminations = []\n",
    "\n",
    "# Here, pin cpw_openRight_end and cpw_openLeft_end are converted into lumped ports,\n",
    "#           each with an impedance of 50 Ohms. <br>\n",
    "port_list = [('cpw_openRight', 'end', 50),\n",
    "                      ('cpw_openLeft', 'end', 50)]\n",
    "jj_to_port = [('Q1', 'rect_jj', 50, False)]\n",
    "# Neither of the junctions in Q1 or Q2 are rendered.\n",
    "ignored_jjs = [('Q2', 'rect_jj')]\n",
    "box_plus_buffer = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "948aa9b4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:57AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 08:57AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 08:57AM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 08:57AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Ansoft/\n",
      "\tProject:   Project24\n",
      "INFO 08:57AM [connect_design]: No active design found (or error getting active design).\n",
      "INFO 08:57AM [connect]: \t Connected to project \"Project24\". No design detected\n",
      "INFO 08:57AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Sweep_DrivenModal_hfss [Solution type: DrivenModal]\n",
      "WARNING 08:57AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 08:57AM [connect_setup]: \tCreating drivenmodal default setup.\n",
      "INFO 08:57AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 08:58AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 08:58AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 08:58AM [analyze]: Analyzing setup Sweep_DrivenModal_setup : Sweep_options__dm_sweep\n",
      "INFO 09:01AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Sweep_DrivenModal_hfss [Solution type: DrivenModal]\n",
      "INFO 09:02AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 09:02AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 09:02AM [analyze]: Analyzing setup Sweep_DrivenModal_setup : Sweep_options__dm_sweep\n",
      "INFO 09:07AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Sweep_DrivenModal_hfss [Solution type: DrivenModal]\n",
      "INFO 09:07AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 09:07AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 09:07AM [analyze]: Analyzing setup Sweep_DrivenModal_setup : Sweep_options__dm_sweep\n"
     ]
    }
   ],
   "source": [
    "#Note: The method will connect to  Ansys simulation, activate_drivenmodal_design(), add_drivenmodal_setup().\n",
    "\n",
    "all_sweeps, return_code = em1.run_sweep(meanderQ1.name,\n",
    "                                        'total_length', \n",
    "                                        ['9mm', '10mm', '11mm'],\n",
    "                                        qcomp_render,\n",
    "                                        open_terminations,\n",
    "                                        design_name=design_name,\n",
    "                                        port_list = port_list,\n",
    "                                        jj_to_port= jj_to_port,\n",
    "                                        ignored_jjs= ignored_jjs,\n",
    "                                        box_plus_buffer=box_plus_buffer\n",
    "                                       )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ee7c0b-c41c-4fde-951b-87bdac17200c",
   "metadata": {
    "tags": []
   },
   "source": [
    "#Note: Sweep again using the arguments from previous run.  \n",
    "```\n",
    "all_sweeps_6_7_8, return_code = em1.run_sweep(meanderQ1.name,\n",
    "                                        'total_length', \n",
    "                                        ['6.5mm', '7.5mm', '8.5mm']\n",
    "                                       )\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c564b8d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['9mm', '10mm', '11mm'])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "if return_code == 0:\n",
    "    # Each key corresponds to list passed to ['9mm', '8mm', '7mm']\n",
    "    print(all_sweeps.keys())\n",
    "    \n",
    "    # Each key corresponds to list passed to ['6mm', '5mm', '4mm']\n",
    "    # print(all_sweeps_6_7_8.keys())\n",
    "else:\n",
    "    print('Check warning messages to see why all_sweeps is non-zero.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6a53f4b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['option_name', 'variables'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sweeps['9mm'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "656a0690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sim_setup_name': 'Sweep_DrivenModal_setup',\n",
       " 'sweep_name': 'Sweep_options__dm_sweep'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sweeps['9mm']['variables']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c5697567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'total_length'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sweeps['9mm']['option_name']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2a8eb01c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "project_name = Project24\n",
      "design_name  = Sweep_DrivenModal_hfss\n",
      "setup_name   = Sweep_DrivenModal_setup\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "project_name = {em1.renderer.pinfo.project_name}\n",
    "design_name  = {em1.renderer.pinfo.design_name}\n",
    "setup_name   = {em1.renderer.pinfo.setup_name}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2002a4",
   "metadata": {},
   "source": [
    "Note: Results storage is currently being updated to be fully functional with the sweep functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1ec8f294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                        Z11                   Z21\n",
       " 4.000  -0.000000-11.896548j   0.000000+44.291514j\n",
       " 4.001   0.000001-11.881640j   0.00000+044.287660j\n",
       " 4.002   0.000001-11.866735j   0.000000+44.283811j\n",
       " 4.003   0.000002-11.851833j   0.000000+44.279968j\n",
       " 4.004   0.000002-11.836933j   0.000000+44.276131j\n",
       " ...                     ...                   ...\n",
       " 8.996  0.000429+233.181228j  0.000398+237.847267j\n",
       " 8.997  0.000331+233.625173j  0.000307+238.283674j\n",
       " 8.998  0.000228+234.070780j  0.000211+238.721763j\n",
       " 8.999  0.000117+234.518060j  0.000109+239.161547j\n",
       " 9.000 -0.000000+234.967023j -0.000000+239.603035j\n",
       " \n",
       " [5001 rows x 2 columns],\n",
       " <Figure size 3000x1800 with 2 Axes>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "em1.get_impedance()                # default: ['Z11', 'Z21']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1e40ac2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                      Y11                 Y21\n",
       " 4.000  0.000000-0.006633j -0.000000-0.024359j\n",
       " 4.001  0.000000-0.006625j  0.000000-0.024357j\n",
       " 4.002  0.000000-0.006617j  0.000000-0.024355j\n",
       " 4.003  0.000000-0.006609j  0.000000-0.024352j\n",
       " 4.004  0.000000-0.006600j  0.000000-0.024350j\n",
       " ...                   ...                 ...\n",
       " 8.996 -0.000000+0.086442j  0.000000-0.088951j\n",
       " 8.997 -0.000000+0.086555j  0.000000-0.089060j\n",
       " 8.998 -0.000000+0.086669j  0.000000-0.089169j\n",
       " 8.999 -0.000000+0.086783j  0.000000-0.089279j\n",
       " 9.000  0.000000+0.086898j -0.000000-0.089390j\n",
       " \n",
       " [5001 rows x 2 columns],\n",
       " <Figure size 3000x1800 with 2 Axes>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "em1.get_admittance()              # default: ['Y11', 'Y21']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4640a8c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                      S11                 S21                 S31\n",
       " 4.000 -0.147086-0.038897j -0.264193+0.952394j  0.000055+0.000028j\n",
       " 4.001 -0.147108-0.038852j -0.263887+0.952478j  0.000055+0.000028j\n",
       " 4.002 -0.147130-0.038806j -0.263581+0.952561j  0.000055+0.000028j\n",
       " 4.003 -0.147152-0.038761j -0.263275+0.952644j  0.000055+0.000028j\n",
       " 4.004 -0.147174-0.038715j -0.262969+0.952727j  0.000055+0.000028j\n",
       " ...                   ...                 ...                 ...\n",
       " 8.996  0.005780-0.006204j  0.976020+0.217516j -0.000039+0.000050j\n",
       " 8.997  0.005783-0.006260j  0.976093+0.217185j -0.000039+0.000050j\n",
       " 8.998  0.005786-0.006317j  0.976166+0.216855j -0.000039+0.000050j\n",
       " 8.999  0.005790-0.006373j  0.976239+0.216524j -0.000039+0.000050j\n",
       " 9.000  0.005793-0.006430j  0.976312+0.216192j -0.000039+0.000050j\n",
       " \n",
       " [5001 rows x 3 columns],\n",
       " <Figure size 3000x1800 with 2 Axes>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "em1.get_scattering(['S11', 'S21', 'S31'])          ## default: ['S11', 'S21', 'S22']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "33a0735c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_scattering = em1.get_scattering(['S11', 'S21', 'S31'])\n",
    "df_s = dataframe_scattering[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "929bb804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.000    0.000055+0.000028j\n",
       "4.001    0.000055+0.000028j\n",
       "4.002    0.000055+0.000028j\n",
       "4.003    0.000055+0.000028j\n",
       "4.004    0.000055+0.000028j\n",
       "                ...        \n",
       "8.996   -0.000039+0.000050j\n",
       "8.997   -0.000039+0.000050j\n",
       "8.998   -0.000039+0.000050j\n",
       "8.999   -0.000039+0.000050j\n",
       "9.000   -0.000039+0.000050j\n",
       "Name: S31, Length: 5001, dtype: complex128"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s11 = df_s['S11']\n",
    "s11\n",
    "\n",
    "s21 = df_s['S21']\n",
    "s21\n",
    "\n",
    "s31 = df_s['S31']\n",
    "s31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fe5079dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S11</th>\n",
       "      <th>S21</th>\n",
       "      <th>S31</th>\n",
       "      <th>20_log_of_mag_S11</th>\n",
       "      <th>20_log_of_mag_S21</th>\n",
       "      <th>20_log_of_mag_S31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4.000</th>\n",
       "      <td>-0.147086-0.038897j</td>\n",
       "      <td>-0.264193+0.952394j</td>\n",
       "      <td>0.000055+0.000028j</td>\n",
       "      <td>-16.355031</td>\n",
       "      <td>-0.101708</td>\n",
       "      <td>-84.181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.001</th>\n",
       "      <td>-0.147108-0.038852j</td>\n",
       "      <td>-0.263887+0.952478j</td>\n",
       "      <td>0.000055+0.000028j</td>\n",
       "      <td>-16.354476</td>\n",
       "      <td>-0.101722</td>\n",
       "      <td>-84.199503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.002</th>\n",
       "      <td>-0.147130-0.038806j</td>\n",
       "      <td>-0.263581+0.952561j</td>\n",
       "      <td>0.000055+0.000028j</td>\n",
       "      <td>-16.353923</td>\n",
       "      <td>-0.101735</td>\n",
       "      <td>-84.217446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.003</th>\n",
       "      <td>-0.147152-0.038761j</td>\n",
       "      <td>-0.263275+0.952644j</td>\n",
       "      <td>0.000055+0.000028j</td>\n",
       "      <td>-16.353370</td>\n",
       "      <td>-0.101748</td>\n",
       "      <td>-84.235327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.004</th>\n",
       "      <td>-0.147174-0.038715j</td>\n",
       "      <td>-0.262969+0.952727j</td>\n",
       "      <td>0.000055+0.000028j</td>\n",
       "      <td>-16.352819</td>\n",
       "      <td>-0.101761</td>\n",
       "      <td>-84.253149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.996</th>\n",
       "      <td>0.005780-0.006204j</td>\n",
       "      <td>0.976020+0.217516j</td>\n",
       "      <td>-0.000039+0.000050j</td>\n",
       "      <td>-41.433315</td>\n",
       "      <td>-0.000314</td>\n",
       "      <td>-83.990805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.997</th>\n",
       "      <td>0.005783-0.006260j</td>\n",
       "      <td>0.976093+0.217185j</td>\n",
       "      <td>-0.000039+0.000050j</td>\n",
       "      <td>-41.388457</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-83.990549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.998</th>\n",
       "      <td>0.005786-0.006317j</td>\n",
       "      <td>0.976166+0.216855j</td>\n",
       "      <td>-0.000039+0.000050j</td>\n",
       "      <td>-41.343861</td>\n",
       "      <td>-0.000319</td>\n",
       "      <td>-83.990273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.999</th>\n",
       "      <td>0.005790-0.006373j</td>\n",
       "      <td>0.976239+0.216524j</td>\n",
       "      <td>-0.000039+0.000050j</td>\n",
       "      <td>-41.299529</td>\n",
       "      <td>-0.000322</td>\n",
       "      <td>-83.989977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.000</th>\n",
       "      <td>0.005793-0.006430j</td>\n",
       "      <td>0.976312+0.216192j</td>\n",
       "      <td>-0.000039+0.000050j</td>\n",
       "      <td>-41.255465</td>\n",
       "      <td>-0.000325</td>\n",
       "      <td>-83.989660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5001 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      S11                 S21                 S31  \\\n",
       "4.000 -0.147086-0.038897j -0.264193+0.952394j  0.000055+0.000028j   \n",
       "4.001 -0.147108-0.038852j -0.263887+0.952478j  0.000055+0.000028j   \n",
       "4.002 -0.147130-0.038806j -0.263581+0.952561j  0.000055+0.000028j   \n",
       "4.003 -0.147152-0.038761j -0.263275+0.952644j  0.000055+0.000028j   \n",
       "4.004 -0.147174-0.038715j -0.262969+0.952727j  0.000055+0.000028j   \n",
       "...                   ...                 ...                 ...   \n",
       "8.996  0.005780-0.006204j  0.976020+0.217516j -0.000039+0.000050j   \n",
       "8.997  0.005783-0.006260j  0.976093+0.217185j -0.000039+0.000050j   \n",
       "8.998  0.005786-0.006317j  0.976166+0.216855j -0.000039+0.000050j   \n",
       "8.999  0.005790-0.006373j  0.976239+0.216524j -0.000039+0.000050j   \n",
       "9.000  0.005793-0.006430j  0.976312+0.216192j -0.000039+0.000050j   \n",
       "\n",
       "       20_log_of_mag_S11  20_log_of_mag_S21  20_log_of_mag_S31  \n",
       "4.000         -16.355031          -0.101708         -84.181500  \n",
       "4.001         -16.354476          -0.101722         -84.199503  \n",
       "4.002         -16.353923          -0.101735         -84.217446  \n",
       "4.003         -16.353370          -0.101748         -84.235327  \n",
       "4.004         -16.352819          -0.101761         -84.253149  \n",
       "...                  ...                ...                ...  \n",
       "8.996         -41.433315          -0.000314         -83.990805  \n",
       "8.997         -41.388457          -0.000317         -83.990549  \n",
       "8.998         -41.343861          -0.000319         -83.990273  \n",
       "8.999         -41.299529          -0.000322         -83.989977  \n",
       "9.000         -41.255465          -0.000325         -83.989660  \n",
       "\n",
       "[5001 rows x 6 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_scattering[0]['20_log_of_mag_S11']= 20 * np.log10(np.absolute(s11))\n",
    "dataframe_scattering[0]['20_log_of_mag_S21']= 20 * np.log10(np.absolute(s21))\n",
    "dataframe_scattering[0]['20_log_of_mag_S31']= 20 * np.log10(np.absolute(s31))\n",
    "dataframe_scattering[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9e405022",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reference to current axis. \n",
    "magnitude = plt.figure('Magnitude S11, S21, and S31')\n",
    "plt.clf()\n",
    "axis = plt.gca() # Get current axis.\n",
    "dataframe_scattering[0].plot(kind = 'line', y='20_log_of_mag_S11', color = 'green', ax = axis)\n",
    "dataframe_scattering[0].plot(kind = 'line', y='20_log_of_mag_S21', color = 'blue', ax = axis)\n",
    "dataframe_scattering[0].plot(kind = 'line', y='20_log_of_mag_S31', color = 'red', ax = axis)\n",
    "plt.title(f'S-Parameter Magnitude')\n",
    "plt.xlabel(f'frequency [GHZ]')\n",
    "plt.ylabel(f'|S11|,|S21|,|S31| [dB]')\n",
    "magnitude.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dfd0b543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S11</th>\n",
       "      <th>S21</th>\n",
       "      <th>S31</th>\n",
       "      <th>20_log_of_mag_S11</th>\n",
       "      <th>20_log_of_mag_S21</th>\n",
       "      <th>20_log_of_mag_S31</th>\n",
       "      <th>degrees_S11</th>\n",
       "      <th>degrees_S21</th>\n",
       "      <th>degrees_S31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4.000</th>\n",
       "      <td>-0.147086-0.038897j</td>\n",
       "      <td>-0.264193+0.952394j</td>\n",
       "      <td>0.000055+0.000028j</td>\n",
       "      <td>-16.355031</td>\n",
       "      <td>-0.101708</td>\n",
       "      <td>-84.181500</td>\n",
       "      <td>-165.187158</td>\n",
       "      <td>105.503950</td>\n",
       "      <td>27.027756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.001</th>\n",
       "      <td>-0.147108-0.038852j</td>\n",
       "      <td>-0.263887+0.952478j</td>\n",
       "      <td>0.000055+0.000028j</td>\n",
       "      <td>-16.354476</td>\n",
       "      <td>-0.101722</td>\n",
       "      <td>-84.199503</td>\n",
       "      <td>-165.205819</td>\n",
       "      <td>105.485570</td>\n",
       "      <td>27.012771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.002</th>\n",
       "      <td>-0.147130-0.038806j</td>\n",
       "      <td>-0.263581+0.952561j</td>\n",
       "      <td>0.000055+0.000028j</td>\n",
       "      <td>-16.353923</td>\n",
       "      <td>-0.101735</td>\n",
       "      <td>-84.217446</td>\n",
       "      <td>-165.224480</td>\n",
       "      <td>105.467191</td>\n",
       "      <td>26.997785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.003</th>\n",
       "      <td>-0.147152-0.038761j</td>\n",
       "      <td>-0.263275+0.952644j</td>\n",
       "      <td>0.000055+0.000028j</td>\n",
       "      <td>-16.353370</td>\n",
       "      <td>-0.101748</td>\n",
       "      <td>-84.235327</td>\n",
       "      <td>-165.243140</td>\n",
       "      <td>105.448812</td>\n",
       "      <td>26.982800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.004</th>\n",
       "      <td>-0.147174-0.038715j</td>\n",
       "      <td>-0.262969+0.952727j</td>\n",
       "      <td>0.000055+0.000028j</td>\n",
       "      <td>-16.352819</td>\n",
       "      <td>-0.101761</td>\n",
       "      <td>-84.253149</td>\n",
       "      <td>-165.261801</td>\n",
       "      <td>105.430433</td>\n",
       "      <td>26.967815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.996</th>\n",
       "      <td>0.005780-0.006204j</td>\n",
       "      <td>0.976020+0.217516j</td>\n",
       "      <td>-0.000039+0.000050j</td>\n",
       "      <td>-41.433315</td>\n",
       "      <td>-0.000314</td>\n",
       "      <td>-83.990805</td>\n",
       "      <td>-47.027252</td>\n",
       "      <td>12.563626</td>\n",
       "      <td>128.377355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.997</th>\n",
       "      <td>0.005783-0.006260j</td>\n",
       "      <td>0.976093+0.217185j</td>\n",
       "      <td>-0.000039+0.000050j</td>\n",
       "      <td>-41.388457</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-83.990549</td>\n",
       "      <td>-47.269117</td>\n",
       "      <td>12.544227</td>\n",
       "      <td>128.350189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.998</th>\n",
       "      <td>0.005786-0.006317j</td>\n",
       "      <td>0.976166+0.216855j</td>\n",
       "      <td>-0.000039+0.000050j</td>\n",
       "      <td>-41.343861</td>\n",
       "      <td>-0.000319</td>\n",
       "      <td>-83.990273</td>\n",
       "      <td>-47.509012</td>\n",
       "      <td>12.524818</td>\n",
       "      <td>128.322852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.999</th>\n",
       "      <td>0.005790-0.006373j</td>\n",
       "      <td>0.976239+0.216524j</td>\n",
       "      <td>-0.000039+0.000050j</td>\n",
       "      <td>-41.299529</td>\n",
       "      <td>-0.000322</td>\n",
       "      <td>-83.989977</td>\n",
       "      <td>-47.746983</td>\n",
       "      <td>12.505400</td>\n",
       "      <td>128.295342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.000</th>\n",
       "      <td>0.005793-0.006430j</td>\n",
       "      <td>0.976312+0.216192j</td>\n",
       "      <td>-0.000039+0.000050j</td>\n",
       "      <td>-41.255465</td>\n",
       "      <td>-0.000325</td>\n",
       "      <td>-83.989660</td>\n",
       "      <td>-47.983072</td>\n",
       "      <td>12.485973</td>\n",
       "      <td>128.267657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5001 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      S11                 S21                 S31  \\\n",
       "4.000 -0.147086-0.038897j -0.264193+0.952394j  0.000055+0.000028j   \n",
       "4.001 -0.147108-0.038852j -0.263887+0.952478j  0.000055+0.000028j   \n",
       "4.002 -0.147130-0.038806j -0.263581+0.952561j  0.000055+0.000028j   \n",
       "4.003 -0.147152-0.038761j -0.263275+0.952644j  0.000055+0.000028j   \n",
       "4.004 -0.147174-0.038715j -0.262969+0.952727j  0.000055+0.000028j   \n",
       "...                   ...                 ...                 ...   \n",
       "8.996  0.005780-0.006204j  0.976020+0.217516j -0.000039+0.000050j   \n",
       "8.997  0.005783-0.006260j  0.976093+0.217185j -0.000039+0.000050j   \n",
       "8.998  0.005786-0.006317j  0.976166+0.216855j -0.000039+0.000050j   \n",
       "8.999  0.005790-0.006373j  0.976239+0.216524j -0.000039+0.000050j   \n",
       "9.000  0.005793-0.006430j  0.976312+0.216192j -0.000039+0.000050j   \n",
       "\n",
       "       20_log_of_mag_S11  20_log_of_mag_S21  20_log_of_mag_S31  degrees_S11  \\\n",
       "4.000         -16.355031          -0.101708         -84.181500  -165.187158   \n",
       "4.001         -16.354476          -0.101722         -84.199503  -165.205819   \n",
       "4.002         -16.353923          -0.101735         -84.217446  -165.224480   \n",
       "4.003         -16.353370          -0.101748         -84.235327  -165.243140   \n",
       "4.004         -16.352819          -0.101761         -84.253149  -165.261801   \n",
       "...                  ...                ...                ...          ...   \n",
       "8.996         -41.433315          -0.000314         -83.990805   -47.027252   \n",
       "8.997         -41.388457          -0.000317         -83.990549   -47.269117   \n",
       "8.998         -41.343861          -0.000319         -83.990273   -47.509012   \n",
       "8.999         -41.299529          -0.000322         -83.989977   -47.746983   \n",
       "9.000         -41.255465          -0.000325         -83.989660   -47.983072   \n",
       "\n",
       "       degrees_S21  degrees_S31  \n",
       "4.000   105.503950    27.027756  \n",
       "4.001   105.485570    27.012771  \n",
       "4.002   105.467191    26.997785  \n",
       "4.003   105.448812    26.982800  \n",
       "4.004   105.430433    26.967815  \n",
       "...            ...          ...  \n",
       "8.996    12.563626   128.377355  \n",
       "8.997    12.544227   128.350189  \n",
       "8.998    12.524818   128.322852  \n",
       "8.999    12.505400   128.295342  \n",
       "9.000    12.485973   128.267657  \n",
       "\n",
       "[5001 rows x 9 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data is shown as degrees.  \n",
    "# However, if you want radians, change value of deg to false, deg=False.\n",
    "dataframe_scattering[0]['degrees_S11'] = np.angle(s11, deg=True)\n",
    "dataframe_scattering[0]['degrees_S21'] = np.angle(s21, deg=True)\n",
    "dataframe_scattering[0]['degrees_S31'] = np.angle(s31, deg=True)\n",
    "dataframe_scattering[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f8fc9895",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reference to current axis. \n",
    "phase = plt.figure('Phase of S11 and S21')\n",
    "plt.clf()\n",
    "axis = plt.gca() # Get current axis.\n",
    "dataframe_scattering[0].plot(kind = 'line', y='degrees_S11', color = 'green', ax = axis)\n",
    "dataframe_scattering[0].plot(kind = 'line', y='degrees_S21', color = 'blue', ax = axis)\n",
    "dataframe_scattering[0].plot(kind = 'line', y='degrees_S31', color = 'red', ax = axis)\n",
    "plt.title(f'S-Parameter Phase')\n",
    "plt.xlabel(f'frequency [GHZ]')\n",
    "plt.ylabel(f'<S11, <S21, <S31 [degrees]')\n",
    "phase.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7646293c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "em1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "33de9236",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment next line if you would like to close the gui\n",
    "# gui.main_window.close()"
   ]
  }
 ],
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   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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